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testcpu.py
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testcpu.py
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#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
from torch.autograd import gradcheck
from dcn_v2 import dcn_v2_conv, DCNv2, DCN
from dcn_v2 import dcn_v2_pooling, DCNv2Pooling, DCNPooling
deformable_groups = 1
N, inC, inH, inW = 2, 2, 4, 4
outC = 2
kH, kW = 3, 3
def conv_identify(weight, bias):
weight.data.zero_()
bias.data.zero_()
o, i, h, w = weight.shape
y = h//2
x = w//2
for p in range(i):
for q in range(o):
if p == q:
weight.data[q, p, y, x] = 1.0
def check_zero_offset():
conv_offset = nn.Conv2d(inC, deformable_groups * 2 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True)
conv_mask = nn.Conv2d(inC, deformable_groups * 1 * kH * kW,
kernel_size=(kH, kW),
stride=(1, 1),
padding=(1, 1),
bias=True)
dcn_v2 = DCNv2(inC, outC, (kH, kW),
stride=1, padding=1, dilation=1,
deformable_groups=deformable_groups)
conv_offset.weight.data.zero_()
conv_offset.bias.data.zero_()
conv_mask.weight.data.zero_()
conv_mask.bias.data.zero_()
conv_identify(dcn_v2.weight, dcn_v2.bias)
input = torch.randn(N, inC, inH, inW)
offset = conv_offset(input)
mask = conv_mask(input)
mask = torch.sigmoid(mask)
output = dcn_v2(input, offset, mask)
output *= 2
d = (input - output).abs().max()
if d < 1e-10:
print('Zero offset passed')
else:
print('Zero offset failed')
print(input)
print(output)
def check_gradient_dconv():
input = torch.rand(N, inC, inH, inW) * 0.01
input.requires_grad = True
offset = torch.randn(N, deformable_groups * 2 * kW * kH, inH, inW) * 2
# offset.data.zero_()
# offset.data -= 0.5
offset.requires_grad = True
mask = torch.rand(N, deformable_groups * 1 * kW * kH, inH, inW)
# mask.data.zero_()
mask.requires_grad = True
mask = torch.sigmoid(mask)
weight = torch.randn(outC, inC, kH, kW)
weight.requires_grad = True
bias = torch.rand(outC)
bias.requires_grad = True
stride = 1
padding = 1
dilation = 1
print('check_gradient_dconv: ',
gradcheck(dcn_v2_conv, (input, offset, mask, weight, bias,
stride, padding, dilation, deformable_groups),
eps=1e-3, atol=1e-4, rtol=1e-2))
def check_pooling_zero_offset():
input = torch.randn(2, 16, 64, 64).zero_()
input[0, :, 16:26, 16:26] = 1.
input[1, :, 10:20, 20:30] = 2.
rois = torch.tensor([
[0, 65, 65, 103, 103],
[1, 81, 41, 119, 79],
]).float()
pooling = DCNv2Pooling(spatial_scale=1.0 / 4,
pooled_size=7,
output_dim=16,
no_trans=True,
group_size=1,
trans_std=0.0)
out = pooling(input, rois, input.new())
s = ', '.join(['%f' % out[i, :, :, :].mean().item()
for i in range(rois.shape[0])])
print(s)
dpooling = DCNv2Pooling(spatial_scale=1.0 / 4,
pooled_size=7,
output_dim=16,
no_trans=False,
group_size=1,
trans_std=0.0)
offset = torch.randn(20, 2, 7, 7).zero_()
dout = dpooling(input, rois, offset)
s = ', '.join(['%f' % dout[i, :, :, :].mean().item()
for i in range(rois.shape[0])])
print(s)
def check_gradient_dpooling():
input = torch.randn(2, 3, 5, 5) * 0.01
N = 4
batch_inds = torch.randint(2, (N, 1)).float()
x = torch.rand((N, 1)).float() * 15
y = torch.rand((N, 1)).float() * 15
w = torch.rand((N, 1)).float() * 10
h = torch.rand((N, 1)).float() * 10
rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1)
offset = torch.randn(N, 2, 3, 3)
input.requires_grad = True
offset.requires_grad = True
spatial_scale = 1.0 / 4
pooled_size = 3
output_dim = 3
no_trans = 0
group_size = 1
trans_std = 0.0
sample_per_part = 4
part_size = pooled_size
print('check_gradient_dpooling:',
gradcheck(dcn_v2_pooling, (input, rois, offset,
spatial_scale,
pooled_size,
output_dim,
no_trans,
group_size,
part_size,
sample_per_part,
trans_std),
eps=1e-4))
def example_dconv():
input = torch.randn(2, 64, 128, 128)
# wrap all things (offset and mask) in DCN
dcn = DCN(64, 64, kernel_size=(3, 3), stride=1,
padding=1, deformable_groups=2)
# print(dcn.weight.shape, input.shape)
output = dcn(input)
targert = output.new(*output.size())
targert.data.uniform_(-0.01, 0.01)
error = (targert - output).mean()
error.backward()
print(output.shape)
def example_dpooling():
input = torch.randn(2, 32, 64, 64)
batch_inds = torch.randint(2, (20, 1)).float()
x = torch.randint(256, (20, 1)).float()
y = torch.randint(256, (20, 1)).float()
w = torch.randint(64, (20, 1)).float()
h = torch.randint(64, (20, 1)).float()
rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1)
offset = torch.randn(20, 2, 7, 7)
input.requires_grad = True
offset.requires_grad = True
# normal roi_align
pooling = DCNv2Pooling(spatial_scale=1.0 / 4,
pooled_size=7,
output_dim=32,
no_trans=True,
group_size=1,
trans_std=0.1)
# deformable pooling
dpooling = DCNv2Pooling(spatial_scale=1.0 / 4,
pooled_size=7,
output_dim=32,
no_trans=False,
group_size=1,
trans_std=0.1)
out = pooling(input, rois, offset)
dout = dpooling(input, rois, offset)
print(out.shape)
print(dout.shape)
target_out = out.new(*out.size())
target_out.data.uniform_(-0.01, 0.01)
target_dout = dout.new(*dout.size())
target_dout.data.uniform_(-0.01, 0.01)
e = (target_out - out).mean()
e.backward()
e = (target_dout - dout).mean()
e.backward()
def example_mdpooling():
input = torch.randn(2, 32, 64, 64)
input.requires_grad = True
batch_inds = torch.randint(2, (20, 1)).float()
x = torch.randint(256, (20, 1)).float()
y = torch.randint(256, (20, 1)).float()
w = torch.randint(64, (20, 1)).float()
h = torch.randint(64, (20, 1)).float()
rois = torch.cat((batch_inds, x, y, x + w, y + h), dim=1)
# mdformable pooling (V2)
dpooling = DCNPooling(spatial_scale=1.0 / 4,
pooled_size=7,
output_dim=32,
no_trans=False,
group_size=1,
trans_std=0.1,
deform_fc_dim=1024)
dout = dpooling(input, rois)
target = dout.new(*dout.size())
target.data.uniform_(-0.1, 0.1)
error = (target - dout).mean()
error.backward()
print(dout.shape)
if __name__ == '__main__':
example_dconv()
example_dpooling()
example_mdpooling()
check_pooling_zero_offset()
# zero offset check
if inC == outC:
check_zero_offset()
check_gradient_dpooling()
check_gradient_dconv()
# """
# ****** Note: backward is not reentrant error may not be a serious problem,
# ****** since the max error is less than 1e-7,
# ****** Still looking for what trigger this problem
# """